Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts

TL;DR

This paper investigates transfer learning for renewable power forecasts, demonstrating that model selection, adaptation, and ensemble methods can significantly enhance accuracy with limited training data.

Contribution

It introduces the first comprehensive study on transfer learning for renewable power forecasts, applying computer vision techniques and Bayesian regression to improve predictions.

Findings

Transfer learning reduces forecast error in renewable power prediction.

Ensemble methods further improve model performance.

Effective with limited training data, e.g., only seven days.

Abstract

There is recent interest in using model hubs, a collection of pre-trained models, in computer vision tasks. To utilize the model hub, we first select a source model and then adapt the model for the target to compensate for differences. While there is yet limited research on model selection and adaption for computer vision tasks, this holds even more for the field of renewable power. At the same time, it is a crucial challenge to provide forecasts for the increasing demand for power forecasts based on weather features from a numerical weather prediction. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent results from the field of computer vision on 667 wind and photovoltaic parks. To the best of our knowledge, this makes it the most extensive study for transfer learning in renewable power forecasts reducing the computational effort and improving the forecast error. Therefore, we adopt source models based on target data from different seasons and limit the amount of training data. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach.